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class sklearn.semi_supervised.LabelPropagation(kernel='rbf', gamma=20, n_neighbors=7, alpha=1, max_iter=30, tol=0.001, n_jobs=1)
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Label Propagation classifier
Read more in the User Guide.
Parameters: kernel : {?knn?, ?rbf?}
String identifier for kernel function to use. Only ?rbf? and ?knn? kernels are currently supported..
gamma : float
Parameter for rbf kernel
n_neighbors : integer > 0
Parameter for knn kernel
alpha : float
Clamping factor
max_iter : float
Change maximum number of iterations allowed
tol : float
Convergence tolerance: threshold to consider the system at steady state
Attributes: X_ : array, shape = [n_samples, n_features]
Input array.
classes_ : array, shape = [n_classes]
The distinct labels used in classifying instances.
label_distributions_ : array, shape = [n_samples, n_classes]
Categorical distribution for each item.
transduction_ : array, shape = [n_samples]
Label assigned to each item via the transduction.
n_iter_ : int
Number of iterations run.
See also
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LabelSpreading
- Alternate label propagation strategy more robust to noise
References
Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University, 2002 http://pages.cs.wisc.edu/~jerryzhu/pub/CMU-CALD-02-107.pdf
Examples
>>> from sklearn import datasets >>> from sklearn.semi_supervised import LabelPropagation >>> label_prop_model = LabelPropagation() >>> iris = datasets.load_iris() >>> random_unlabeled_points = np.where(np.random.randint(0, 2, ... size=len(iris.target))) >>> labels = np.copy(iris.target) >>> labels[random_unlabeled_points] = -1 >>> label_prop_model.fit(iris.data, labels) ... LabelPropagation(...)
Methods
fit
(X, y)Fit a semi-supervised label propagation model based get_params
([deep])Get parameters for this estimator. predict
(X)Performs inductive inference across the model. predict_proba
(X)Predict probability for each possible outcome. score
(X, y[, sample_weight])Returns the mean accuracy on the given test data and labels. set_params
(\*\*params)Set the parameters of this estimator. -
__init__(kernel='rbf', gamma=20, n_neighbors=7, alpha=1, max_iter=30, tol=0.001, n_jobs=1)
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fit(X, y)
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Fit a semi-supervised label propagation model based
All the input data is provided matrix X (labeled and unlabeled) and corresponding label matrix y with a dedicated marker value for unlabeled samples.
Parameters: X : array-like, shape = [n_samples, n_features]
A {n_samples by n_samples} size matrix will be created from this
y : array_like, shape = [n_samples]
n_labeled_samples (unlabeled points are marked as -1) All unlabeled samples will be transductively assigned labels
Returns: self : returns an instance of self.
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get_params(deep=True)
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Get parameters for this estimator.
Parameters: deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
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predict(X)
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Performs inductive inference across the model.
Parameters: X : array_like, shape = [n_samples, n_features]
Returns: y : array_like, shape = [n_samples]
Predictions for input data
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predict_proba(X)
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Predict probability for each possible outcome.
Compute the probability estimates for each single sample in X and each possible outcome seen during training (categorical distribution).
Parameters: X : array_like, shape = [n_samples, n_features]
Returns: probabilities : array, shape = [n_samples, n_classes]
Normalized probability distributions across class labels
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score(X, y, sample_weight=None)
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Returns the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters: X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
Returns: score : float
Mean accuracy of self.predict(X) wrt. y.
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set_params(**params)
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Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it?s possible to update each component of a nested object.Returns: self :
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semi_supervised.LabelPropagation()
2017-01-15 04:25:27
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